How we deliver on collaborative data governance

Step One: Data discovery and cleansing

It is crucial to remember that the foundation of your data strategy lies in data discovery. Neglecting to profile your data puts your entire strategy at risk. Analysing the ground and building your "data house" on solid foundations is essential for success.

To facilitate auto-profiling for non-technical users we recommend implementing an enterprise data catalog. A modern data catalog will provide automated discovery and intelligent documentation of the datasets in your data lake, with profiling and sampling capabilities that help you to assess data at a glance. With trusted and autoprofiled datasets, you can have powerful and visual profiling indicators, so users can easily find the right data in a few clicks.

In addition to data discovery, data cleansing is typically intertwined. Our team will collaborate with you to ensure data integrity throughout your pipelines by empowering you to delegate data cleansing through self-service.

A successful data governance framework necessitates assigning responsibilities and granting appropriate authority, all while maintaining control. With a modern data platform, you'll have access to self-service data preparation capabilities, enabling authorised users to effortlessly access a dataset and then cleanse, standardise, transform, or enrich the data.

Step Two: Organise data you can trust and empower people

We will work with you to establish a central point of trust.

Once we have integrated and organised your data into the catalog, and it is consolidated in one location, our focus shifts to operationalising it. This involves providing access to participants in your data value chain, enabling them to immediately utilise, safeguard, curate, and allow a wide range of individuals and applications to benefit from it.

Some of the capabilities we'll arm you with are business glossaries, data lineage, stewardship campaigns, and data masking.

Step Three: Automate your data pipelines and enable data access

We harness the power of machine learning to tap into the knowledge from business users and data professionals.

One such use case is data error resolution and matching. By using self-service tools for deduplicating records on a data sample, we can then apply machine learning to the whole data set into a fully automated process, thus turning low-value and time-consuming tasks into an automated process you can use at scale on millions of records.

Machine learning helps to suggest the next best action to apply to the data pipeline or capture tacit knowledge from the users and run it at scale through automation.